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Thermal Infrared Pedestrian Image Segmentation Using Level Set Method.

Yulong Qiao¹, Ziwei Wei², Yan Zhao³

¹School of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China. qiaoyulong@hrbeu.edu.cn.

Sensors (Basel, Switzerland)

|August 8, 2017

Summary

This summary is machine-generated.

This study introduces an improved level set method for segmenting infrared thermal pedestrian images. The novel approach enhances target boundaries and reduces intensity variations for more accurate contour detection.

Keywords:

active contour model edge indicator function level set method one-bit transform thermal pedestrian images

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Area of Science:

Computer Vision
Image Processing
Artificial Intelligence

Background:

Edge-based active contour models and level set methods are standard for image segmentation.
Traditional methods struggle with infrared thermal pedestrian images due to poor boundary definition and intensity inhomogeneity.

Purpose of the Study:

To develop a novel level set-based segmentation method for infrared thermal pedestrian images.
To address challenges of poorly defined boundaries and intensity inhomogeneity in thermal imagery.

Main Methods:

Utilized a one-bit transform convolution kernel and a soft mark to enhance target boundaries.
Introduced a weight function for adaptive intensity adjustment to mitigate inhomogeneity.
Integrated these enhancements into a level set formulation for adaptive edge indication.

Main Results:

The proposed method successfully enhances target boundaries in infrared thermal images.
Adaptive intensity adjustment effectively reduces inhomogeneity, improving segmentation accuracy.
Experimental results on benchmark datasets demonstrate superior performance compared to state-of-the-art methods.

Conclusions:

The novel level set-based method provides a robust solution for infrared thermal pedestrian image segmentation.
The technique effectively overcomes limitations of traditional methods in challenging thermal imaging scenarios.
This approach offers significant improvements for applications requiring accurate pedestrian detection in thermal imagery.